VisMatchmaker: Cooperation of the User and the Computer in Centralized Matching Adjustment

Abstract

Centralized matching is a ubiquitous resource allocation problem. In a centralized matching problem, each agent has a preference list ranking the other agents and a central planner is responsible for matching the agents manually or with an algorithm. While algorithms can find a matching which optimizes some performance metrics, they are used as a black box and preclude the central planner from applying his domain knowledge to find a matching which aligns better with the user tasks. Furthermore, the existing matching visualization techniques (i.e. bipartite graph and adjacency matrix) fail in helping the central planner understand the differences between matchings. In this paper, we present VisMatchmaker, a visualization system which allows the central planner to explore alternatives to an algorithm-generated matching. We identified three common tasks in the process of matching adjustment: problem detection, matching recommendation and matching evaluation. We classified matching comparison into three levels and designed visualization techniques for them, including the number line view and the stacked graph view. Two types of algorithmic support, namely direct assignment and range search, and their interactive operations are also provided to enable the user to apply his domain knowledge in matching adjustment.

A Collaborative Metaverse based A-La-Carte Framework for Tertiary Education (CO-MATE)

“…Educational Positioning System (EPS) to facilitate navigational tools for educational journey [153] , PABED (Project Analyzing Big Education Data) - a tool implementing Google BigQuery and R programming language for yearly comparison of undergraduate enrollment data [154] , visualization tools like AxiSketcher [155] , VisMatchmaker [156] , VisFlow [157] and senseMap [158] for map based method are some key edu-tech solutions using Big Data Technology.…”

IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality

“…AlgoCrowd [48] visualized the AI-driven matching between workers and tasks in a dashboard, showing the distributions of worker reputation and productivity, the Jain fairness index over rounds of task allocation, and an argumentation-based explanation of why each worker was allocated. VisMatchmaker [49] supports the comparison of trade-offs between two solutions for a job allocation task using novel but unfamiliar visualizations (number lines and glyphs). To promote adoption, it is preferable to use visualizations that are familiar to each domain.…”

“…Talen visualized the distribution of accessibility using geographical maps that are familiar to urban planners [50]. Similar to [49], FairVizARD [51] enables comparing the outcomes between two matching algorithms for ride-sharing, visualizing with a map view of taxi and request locations, and a graph view showing the time series variation of several indicators. In IF-City, we leverage well-known geographical map, heatmap, and bar chart visualizations to convey information about inequality, benefits, and accessibility.…”

A Survey on the Visual Analytics for Data Ranking

“…坐标轴位置表现数据对象大小关系更加直观, 易于理解. VisMatchmaker [15] 利用散点图中点的位 置差异, 直观地表示对象的受欢迎程度及其收入 情况. 可视分析系统 TelCoVis [16] 利用平行坐标轴 上的位置关系, 直观地表达和比较不如图 1 所示同 类别特征在属性轴上的分布.…”